Smart Agriculture in India: Advancements in Image Processing for Automated Plant Disease Detection and Crop Analysis

Year : 2025 | Volume : 12 | Issue : 03 | Page : 13 19
    By

    Suraj Rajbhar,

  • Ashish Patel,

  1. Research Scholar, Master of Computer Applications, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India
  2. Research Scholar, Master of Computer Applications, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai, Maharashtra, India

Abstract

The adoption of image processing technologies in agriculture is emerging as a revolutionary method for tackling persistent challenges in the farming industry. These techniques are increasingly used for different tasks such as detecting plant diseases, assessing crop health, and predicting yields, especially in the framework of smart agriculture systems. This study paints a detailed picture of the latest progress in image processing techniques applied to automated disease detection and detailed crop analysis, with a focus on their relevance and possible consequences on Indian agriculture. Key image processing techniques, including image acquisition, preprocessing, segmentation, feature extraction, and classification, serve as essential building blocks of intelligent agricultural systems. When integrated with machine learning and deep learning models, these methods enable the early and accurate identification of plant diseases, monitoring of plant and soil health, and reliable yield forecasting. These applications support timely decision-making by farmers, helping to reduce labor demands, minimize input costs, and prevent resource wastage through precision farming. India’s agricultural landscape is highly diverse due to its wide range of climatic zones, geographical conditions, and soil types, resulting in region-specific farming practices. In this context, image processing-based solutions provide substantial value due to their adaptability and scalability. These technologies present promising opportunities for both small-scale and large-scale farmers by enabling real-time monitoring and automation of farming processes. However, in spite of their benefits, several hurdles persist, such as limited technological infrastructure in rural areas, a missing set of standard agricultural datasets, and the necessity for region-specific model training. This study explores the current developments in image processing applications within agriculture, outlines key use cases relevant to Indian farming systems, and identifies existing limitations along with future research opportunities. Emphasis is placed on the role of these innovations in advancing eco-friendly farming, improving crop management, and enhancing food security, contributing significantly to India’s evolving agricultural landscape.

Keywords: Smart agriculture, image processing, plant disease detection, crop analysis, automated detection

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
Suraj Rajbhar, Ashish Patel. Smart Agriculture in India: Advancements in Image Processing for Automated Plant Disease Detection and Crop Analysis. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(03):13-19.
How to cite this URL:
Suraj Rajbhar, Ashish Patel. Smart Agriculture in India: Advancements in Image Processing for Automated Plant Disease Detection and Crop Analysis. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(03):13-19. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=215539


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Regular Issue Subscription Original Research
Volume 12
Issue 03
Received 10/03/2025
Accepted 29/06/2025
Published 01/07/2025
Publication Time 113 Days
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